I'm on a mission to dismantle the barriers between technology and the human experience. I believe in pursuing the elegant integration of technology in our everyday actions and interactions.

Monoql

Founder | CEO

I built Monoql to empower mental health providers with AI tools to save time, reduce burnout, and improve the quality of patient care. The healthcare industry has historically lacked automation tools that facilitate patient workflows. Monoql aims to change that by integrating directly into electronic health records (EHRs) and augmenting visibility into a patient's mental health history.

We provide the following tools:

  • Oracle™ - Patient History Summarization
  • Copilot™ - Reporting Automation Tools
  • OmniGraph™ - A Patient Relationship Graph

We are a team of clinicians, data scientists, and engineers working together to build the next generation of mental health tools. We are currently in private beta and are working with a select group of providers to refine our tools.

Guidance Analytics

Principal Engineer

I joined Guidance Analytics to design and build their healthcare analytics platform. I had the opportunity to hire and work with a team of talented engineers to build the Abacus platform and DataLux data visualization tool. Our goal was to provide healthcare organizations with a platform to analyze and visualize their data to make better decisions, as well as guide them on their journey to improving value-based care.

Abacus is a Rust project built with Cargo Workspaces, enabling us to build the core analytics platform as well as various auxiliary tools such as a web server and a remote CLI tool to control it. It handles all large-scale data processing for Guidance Analytics and has modules for time-series analysis, cluster analysis, and natural language processing, as well as computing performance on all available CMS MIPS & QCDR Measures for individual providers and groups.

DataLux is a web interface for Abacus, built with NextJS and React, enabling healthcare providers and administrators to visualize and gain insights into their data. It provides real-time insight into their performance and quality measures as well as patient analytics.

Meta

Data Scientist

I joined Meta as a Data Scientist to work on their Messenger Notifications platform. I was responsible for building and maintaining machine learning models to predict user engagement with notifications. I also worked on the data infrastructure to support these models and the data pipelines to collect and process the data. I worked closely with the product and engineering teams to integrate these models into the product and to measure their impact on user engagement.

I had the opportunity to work with a team of talented data scientists, engineers, and designers to redefine the way we interact with notifications, and to integrate data driven insights to make them less intrusive and more engaging.

WeWork

Applied Science Researcher

I joined WeWork pre-IPO to build tools for generative space planning to automate their design process upon property acquisition. We worked closely with architects and designers to build AI tools that integrated directly with their workflow and augmented their ability to design spaces that not only deterministically met requirements for building code and business projections, but also to create human-centric spaces that feel good to be in. Our goal was not to replace architects and designers, but empower them to create better work, faster.

I'm proud to say that if you've worked at a WeWork building, you've likely been in a space my tools have helped build. :)